[−][src]Trait opencv::prelude::LogisticRegression
Required methods
pub fn as_raw_LogisticRegression(&self) -> *const c_void
[src]
pub fn as_raw_mut_LogisticRegression(&mut self) -> *mut c_void
[src]
Provided methods
pub fn get_learning_rate(&self) -> Result<f64>
[src]
pub fn set_learning_rate(&mut self, val: f64) -> Result<()>
[src]
pub fn get_iterations(&self) -> Result<i32>
[src]
pub fn set_iterations(&mut self, val: i32) -> Result<()>
[src]
pub fn get_regularization(&self) -> Result<i32>
[src]
pub fn set_regularization(&mut self, val: i32) -> Result<()>
[src]
Kind of regularization to be applied. See LogisticRegression::RegKinds.
See also
setRegularization getRegularization
pub fn get_train_method(&self) -> Result<i32>
[src]
pub fn set_train_method(&mut self, val: i32) -> Result<()>
[src]
Kind of training method used. See LogisticRegression::Methods.
See also
setTrainMethod getTrainMethod
pub fn get_mini_batch_size(&self) -> Result<i32>
[src]
Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It has to take values less than the total number of training samples.
See also
setMiniBatchSize
pub fn set_mini_batch_size(&mut self, val: i32) -> Result<()>
[src]
Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It has to take values less than the total number of training samples.
See also
setMiniBatchSize getMiniBatchSize
pub fn get_term_criteria(&self) -> Result<TermCriteria>
[src]
pub fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()>
[src]
pub fn predict(
&self,
samples: &dyn ToInputArray,
results: &mut dyn ToOutputArray,
flags: i32
) -> Result<f32>
[src]
&self,
samples: &dyn ToInputArray,
results: &mut dyn ToOutputArray,
flags: i32
) -> Result<f32>
Predicts responses for input samples and returns a float type.
Parameters
- samples: The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.
- results: Predicted labels as a column matrix of type CV_32S.
- flags: Not used.
C++ default parameters
- results: noArray()
- flags: 0
pub fn get_learnt_thetas(&self) -> Result<Mat>
[src]
This function returns the trained parameters arranged across rows.
For a two class classification problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.
Implementations
impl<'_> dyn LogisticRegression + '_
[src]
pub fn create() -> Result<Ptr<dyn LogisticRegression>>
[src]
Creates empty model.
Creates Logistic Regression model with parameters given.
pub fn load(
filepath: &str,
node_name: &str
) -> Result<Ptr<dyn LogisticRegression>>
[src]
filepath: &str,
node_name: &str
) -> Result<Ptr<dyn LogisticRegression>>
Loads and creates a serialized LogisticRegression from a file
Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
Parameters
- filepath: path to serialized LogisticRegression
- nodeName: name of node containing the classifier
C++ default parameters
- node_name: String()